High Throughput Traction Force Microscopy Using PDMS Reveals Dose-Dependent Effects of Transforming Growth Factor-β on the Epithelial-to-Mesenchymal Transition
Bibliographic record
Abstract
Cellular contractility is essential in diverse aspects of biology, driving processes that range from motility and division, to tissue contraction and mechanical stability, and represents a core element of multi-cellular animal life. In adherent cells, acto-myosin contraction is seen in traction forces that cells exert on their substrate. Dysregulation of cellular contractility appears in a myriad of pathologies, making contractility a promising target in diverse diagnostic approaches using biophysics as a metric. Moreover, novel therapeutic strategies can be based on correcting the apparent malfunction of cell contractility. These applications, however, require direct quantification of these forces. We have developed silicone elastomer-based traction force microscopy (TFM) in a parallelized multi-well format. Our use of a silicone rubber, specifically polydimethylsiloxane (PDMS), rather than the commonly employed hydrogel polyacrylamide (PAA) enables us to make robust and inert substrates with indefinite shelf-lives requiring no specialized storage conditions. Unlike pillar-PDMS based approaches that have a modulus in the GPa range, the PDMS used here is very compliant, ranging from approximately 0.4 kPa to 100 kPa. We create a high-throughput platform for TFM by partitioning these large monolithic substrates spatially into biochemically independent wells, creating a multi-well platform for traction force screening that is compatible with existing multi-well systems. In this manuscript, we use this multi-well traction force system to examine the Epithelial to Mesenchymal Transition (EMT); we induce EMT in NMuMG cells by exposing them to TGF-β, and to quantify the biophysical changes during EMT. We measure the contractility as a function of concentration and duration of TGF-β exposure. Our findings here demonstrate the utility of parallelized TFM in the context of disease biophysics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".